FENDA-FL: Personalized Federated Learning on Heterogeneous Clinical Datasets

Federated learning (FL) is increasingly being recognized as a key approach to overcoming the data silos that so frequently obstruct the training and deployment of machine-learning models in clinical settings. This work contributes to a growing body of FL research specifically focused on clinical applications along three important directions. First, an extension of the FENDA method (Kim et al., 2016) to the FL setting is proposed. Experiments conducted on the FLamby benchmarks (du Terrail et al., 2022a) and GEMINI datasets (Verma et al., 2017) show that the approach is robust to heterogeneous clinical data and often outperforms existing global and personalized FL techniques. Further, the experimental results represent substantive improvements over the original FLamby benchmarks and expand such benchmarks to include evaluation of personalized FL methods. Finally, we advocate for a comprehensive checkpointing and evaluation framework for FL to better reflect practical settings and provide multiple baselines for comparison.